id,node_id,number,title,user,state,locked,assignee,milestone,comments,created_at,updated_at,closed_at,author_association,active_lock_reason,draft,pull_request,body,reactions,performed_via_github_app,state_reason,repo,type 1680031454,I_kwDOAMm_X85kIz7e,7780,mypy does not understand output of binary operations,14371165,open,0,,,8,2023-04-23T13:38:55Z,2024-04-28T20:07:04Z,,MEMBER,,,,"### What happened? When doing operations on numpy arrays and xarray variables mypy does not understand that the output is always a xarray variable regardless of the order. See example. ### What did you expect to happen? mypy to pass for the example code. ### Minimal Complete Verifiable Example ```Python import numpy as np import xarray as xr x = np.array([1, 2, 4]) v = xr.Variable([""x""], x) # numpy first: xv = x * v xv.values # error: ""ndarray[Any, dtype[bool_]]"" has no attribute ""values"" [attr-defined] if isinstance(xv, xr.Variable): xv.values # variable first: vx = v * x vx.values if isinstance(vx, xr.Variable): vx.values ``` ### MVCE confirmation - [X] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray. - [X] Complete example — the example is self-contained, including all data and the text of any traceback. - [X] Verifiable example — the example copy & pastes into an IPython prompt or [Binder notebook](https://mybinder.org/v2/gh/pydata/xarray/main?urlpath=lab/tree/doc/examples/blank_template.ipynb), returning the result. - [X] New issue — a search of GitHub Issues suggests this is not a duplicate. ### Relevant log output _No response_ ### Anything else we need to know? Seen in #7741 ### Environment
xr.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 3.9.16 (main, Mar 8 2023, 10:39:24) [MSC v.1916 64 bit (AMD64)] python-bits: 64 OS: Windows OS-release: 10 machine: AMD64 processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel byteorder: little LC_ALL: None LANG: en libhdf5: 1.10.6 libnetcdf: None xarray: 2023.4.2 pandas: 2.0.0 numpy: 1.23.5 scipy: 1.10.1 netCDF4: None pydap: None h5netcdf: None h5py: 2.10.0 Nio: None zarr: None cftime: None nc_time_axis: None PseudoNetCDF: None iris: None bottleneck: None dask: 2023.4.0 distributed: 2023.4.0 matplotlib: 3.5.3 cartopy: None seaborn: 0.12.2 numbagg: None fsspec: 2023.4.0 cupy: None pint: None sparse: None flox: None numpy_groupies: None setuptools: 67.7.1 pip: 23.1.1 conda: 23.3.1 pytest: 7.3.1 mypy: 1.2.0 IPython: 8.12.0 sphinx: 6.1.3
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/7780/reactions"", ""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue 2215603817,I_kwDOAMm_X86ED25p,8892,ffill's tolerance argument can be strings,14371165,open,0,,,1,2024-03-29T15:49:40Z,2024-04-02T01:50:34Z,,MEMBER,,,,"### What happened? `ffill`, `bfill` `reindex` etc. have tolerance arguments that also supports strings. And we test for it here: https://github.com/pydata/xarray/blob/2120808bbe45f3d4f0b6a01cd43bac4df4039092/xarray/tests/test_groupby.py#L2016-L2025 But our typing assumes it's floats only: https://github.com/pydata/xarray/blob/2120808bbe45f3d4f0b6a01cd43bac4df4039092/xarray/core/resample.py#L69-L94 ### What did you expect to happen? Since our pytests pass, mypy should pass as well. ### Minimal Complete Verifiable Example ```python import numpy as np import pandas as pd import xarray as xr # https://github.com/pydata/xarray/blob/2120808bbe45f3d4f0b6a01cd43bac4df4039092/xarray/tests/test_groupby.py#L2016 # Test tolerance keyword for upsample methods bfill, pad, nearest times = pd.date_range(""2000-01-01"", freq=""1D"", periods=2) times_upsampled = pd.date_range(""2000-01-01"", freq=""6h"", periods=5) array = xr.DataArray(np.arange(2), [(""time"", times)]) # Forward fill actual = array.resample(time=""6h"").ffill(tolerance=""12h"") expected = xr.DataArray([0.0, 0.0, 0.0, np.nan, 1.0], [(""time"", times_upsampled)]) xr.testing.assert_identical(expected, actual) ``` ### Environment master ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/8892/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue 1718410975,I_kwDOAMm_X85mbN7f,7856,Unrecognized chunk manager dask - must be one of: [],14371165,closed,0,,,11,2023-05-21T08:07:57Z,2024-03-27T19:09:18Z,2023-05-24T16:26:20Z,MEMBER,,,,"### What happened? I have just updated my development branch of xarray to latest main. No other changes. When using `.chunk()` on a Variable xarray crashes. ### What did you expect to happen? No crash ### Minimal Complete Verifiable Example ```Python import numpy as np import pandas as pd import xarray as xr t_size = 8000 t = np.arange(t_size) var = xr.Variable(dims=(""T"",), data=np.random.randn(t_size)).chunk() ``` ### MVCE confirmation - [X] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray. - [X] Complete example — the example is self-contained, including all data and the text of any traceback. - [X] Verifiable example — the example copy & pastes into an IPython prompt or [Binder notebook](https://mybinder.org/v2/gh/pydata/xarray/main?urlpath=lab/tree/doc/examples/blank_template.ipynb), returning the result. - [X] New issue — a search of GitHub Issues suggests this is not a duplicate. ### Relevant log output ```Python Traceback (most recent call last): File ""C:\Users\J.W\AppData\Local\Temp\ipykernel_6480\4053253683.py"", line 8, in var = xr.Variable(dims=(""T"",), data=np.random.randn(t_size)).chunk() File ""C:\Users\J.W\Documents\GitHub\xarray\xarray\core\variable.py"", line 1249, in chunk chunkmanager = guess_chunkmanager(chunked_array_type) File ""C:\Users\J.W\Documents\GitHub\xarray\xarray\core\parallelcompat.py"", line 87, in guess_chunkmanager raise ValueError( ValueError: unrecognized chunk manager dask - must be one of: [] ``` ### Anything else we need to know? Likely from #7019. ### Environment
xr.show_versions() C:\Users\J.W\anaconda3\envs\xarray-tests\lib\site-packages\_distutils_hack\__init__.py:33: UserWarning: Setuptools is replacing distutils. warnings.warn(""Setuptools is replacing distutils."") INSTALLED VERSIONS ------------------ commit: None python: 3.10.6 | packaged by conda-forge | (main, Aug 22 2022, 20:30:19) [MSC v.1929 64 bit (AMD64)] python-bits: 64 OS: Windows OS-release: 10 machine: AMD64 processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel byteorder: little LC_ALL: None LANG: en LOCALE: ('Swedish_Sweden', '1252') libhdf5: 1.12.2 libnetcdf: 4.8.1 xarray: 2022.9.1.dev266+gbd01f9cc.d20221006 pandas: 1.5.2 numpy: 1.23.5 scipy: 1.9.3 netCDF4: 1.6.0 pydap: installed h5netcdf: 1.0.2 h5py: 3.7.0 Nio: None zarr: 2.13.2 cftime: 1.6.2 nc_time_axis: 1.4.1 PseudoNetCDF: 3.2.2 iris: 3.3.0 bottleneck: 1.3.5 dask: 2022.9.2 distributed: 2022.9.2 matplotlib: 3.6.2 cartopy: 0.21.0 seaborn: 0.13.0.dev0 numbagg: 0.2.1 fsspec: 2022.10.0 cupy: None pint: 0.19.2 sparse: 0.13.0 flox: 999 numpy_groupies: 0.9.14+22.g19c7601 setuptools: 65.5.1 pip: 22.3.1 conda: None pytest: 7.2.0 mypy: 1.2.0 IPython: 7.33.0 sphinx: 5.3.0
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/7856/reactions"", ""total_count"": 2, ""+1"": 2, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 1797233538,I_kwDOAMm_X85rH5uC,7971,Pint errors on python 3.11 and windows,14371165,closed,0,,,2,2023-07-10T17:44:51Z,2024-02-26T17:52:50Z,2024-02-26T17:52:50Z,MEMBER,,,,"### What happened? The CI seems to consistently crash on `test_units.py` now: ``` =========================== short test summary info =========================== FAILED xarray/tests/test_units.py::TestVariable::test_aggregation[int32-method_max] - TypeError: no implementation found for 'numpy.max' on types that implement __array_function__: [] FAILED xarray/tests/test_units.py::TestVariable::test_aggregation[int32-method_min] - TypeError: no implementation found for 'numpy.min' on types that implement __array_function__: [] FAILED xarray/tests/test_units.py::TestDataArray::test_aggregation[float64-function_max] - TypeError: no implementation found for 'numpy.max' on types that implement __array_function__: [] FAILED xarray/tests/test_units.py::TestDataArray::test_aggregation[float64-function_min] - TypeError: no implementation found for 'numpy.min' on types that implement __array_function__: [] FAILED xarray/tests/test_units.py::TestDataArray::test_aggregation[int32-function_max] - TypeError: no implementation found for 'numpy.max' on types that implement __array_function__: [] FAILED xarray/tests/test_units.py::TestDataArray::test_aggregation[int32-function_min] - TypeError: no implementation found for 'numpy.min' on types that implement __array_function__: [] FAILED xarray/tests/test_units.py::TestDataArray::test_aggregation[int32-method_max] - TypeError: no implementation found for 'numpy.max' on types that implement __array_function__: [] FAILED xarray/tests/test_units.py::TestDataArray::test_aggregation[int32-method_min] - TypeError: no implementation found for 'numpy.min' on types that implement __array_function__: [] FAILED xarray/tests/test_units.py::TestDataArray::test_unary_operations[float64-round] - TypeError: no implementation found for 'numpy.round' on types that implement __array_function__: [] FAILED xarray/tests/test_units.py::TestDataArray::test_unary_operations[int32-round] - TypeError: no implementation found for 'numpy.round' on types that implement __array_function__: [] FAILED xarray/tests/test_units.py::TestDataset::test_aggregation[int32-method_max] - TypeError: no implementation found for 'numpy.max' on types that implement __array_function__: [] FAILED xarray/tests/test_units.py::TestDataset::test_aggregation[int32-method_min] - TypeError: no implementation found for 'numpy.min' on types that implement __array_function__: [] = 12 failed, 14880 passed, 1649 skipped, 146 xfailed, 68 xpassed, 574 warnings in 737.19s (0:12:17) = ``` For more details: https://github.com/pydata/xarray/actions/runs/5438369625/jobs/9889561685?pr=7955 ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/7971/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 1691206894,I_kwDOAMm_X85kzcTu,7802,Mypy errors with matplotlib 3.8,14371165,closed,0,,,6,2023-05-01T19:03:51Z,2023-09-17T05:03:00Z,2023-09-17T05:02:59Z,MEMBER,,,,"Matplotlib has started to support typing in main (https://github.com/matplotlib/matplotlib/issues/20504) and mypy is throwing a few errors:
``` xarray/core/options.py:12: error: Cannot assign to a type [misc] xarray/core/options.py:12: error: Incompatible types in assignment (expression has type ""Type[str]"", variable has type ""Type[Colormap]"") [assignment] xarray/plot/utils.py:808: error: Argument 1 to ""set_xticks"" of ""_AxesBase"" has incompatible type ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]""; expected ""Iterable[float]"" [arg-type] xarray/plot/utils.py:810: error: Argument 1 to ""set_yticks"" of ""_AxesBase"" has incompatible type ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]""; expected ""Iterable[float]"" [arg-type] xarray/plot/utils.py:813: error: Argument 1 to ""set_xlim"" of ""_AxesBase"" has incompatible type ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]""; expected ""Union[float, Tuple[float, float], None]"" [arg-type] xarray/plot/utils.py:815: error: Argument 1 to ""set_ylim"" of ""_AxesBase"" has incompatible type ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]""; expected ""Union[float, Tuple[float, float], None]"" [arg-type] Generated Cobertura report: /home/runner/work/xarray/xarray/mypy_report/cobertura.xml Installing missing stub packages: /home/runner/micromamba-root/envs/xarray-tests/bin/python -m pip install types-Pillow types-PyYAML types-Pygments types-babel types-colorama types-paramiko types-psutil types-pytz types-pywin32 types-setuptools types-urllib3 Generated Cobertura report: /home/runner/work/xarray/xarray/mypy_report/cobertura.xml Found 154 errors in 10 files (checked 138 source files) xarray/plot/utils.py:1349: error: Unsupported operand types for * (""_SupportsArray[dtype[Any]]"" and ""float"") [operator] xarray/plot/utils.py:1349: error: Unsupported operand types for * (""_NestedSequence[_SupportsArray[dtype[Any]]]"" and ""float"") [operator] xarray/plot/utils.py:1349: error: Unsupported operand types for * (""_NestedSequence[Union[bool, int, float, complex, str, bytes]]"" and ""float"") [operator] xarray/plot/utils.py:1349: note: Left operand is of type ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"" xarray/plot/utils.py:1349: error: Unsupported operand types for * (""str"" and ""float"") [operator] xarray/plot/utils.py:1349: error: Unsupported operand types for * (""bytes"" and ""float"") [operator] xarray/plot/utils.py:1350: error: Item ""_SupportsArray[dtype[Any]]"" of ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"" has no attribute ""mask"" [union-attr] xarray/plot/utils.py:1350: error: Item ""_NestedSequence[_SupportsArray[dtype[Any]]]"" of ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"" has no attribute ""mask"" [union-attr] xarray/plot/utils.py:1350: error: Item ""int"" of ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"" has no attribute ""mask"" [union-attr] xarray/plot/utils.py:1350: error: Item ""float"" of ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"" has no attribute ""mask"" [union-attr] xarray/plot/utils.py:1350: error: Item ""complex"" of ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"" has no attribute ""mask"" [union-attr] xarray/plot/utils.py:1350: error: Item ""str"" of ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"" has no attribute ""mask"" [union-attr] xarray/plot/utils.py:1350: error: Item ""bytes"" of ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"" has no attribute ""mask"" [union-attr] xarray/plot/utils.py:1350: error: Item ""_NestedSequence[Union[bool, int, float, complex, str, bytes]]"" of ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"" has no attribute ""mask"" [union-attr] xarray/plot/utils.py:1351: error: Item ""_SupportsArray[dtype[Any]]"" of ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"" has no attribute ""mask"" [union-attr] xarray/plot/utils.py:1351: error: Item ""_NestedSequence[_SupportsArray[dtype[Any]]]"" of ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"" has no attribute ""mask"" [union-attr] xarray/plot/utils.py:1351: error: Item ""int"" of ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"" has no attribute ""mask"" [union-attr] xarray/plot/utils.py:1351: error: Item ""float"" of ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"" has no attribute ""mask"" [union-attr] xarray/plot/utils.py:1351: error: Item ""complex"" of ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"" has no attribute ""mask"" [union-attr] xarray/plot/utils.py:1351: error: Item ""str"" of ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"" has no attribute ""mask"" [union-attr] xarray/plot/utils.py:1351: error: Item ""bytes"" of ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"" has no attribute ""mask"" [union-attr] xarray/plot/utils.py:1351: error: Item ""_NestedSequence[Union[bool, int, float, complex, str, bytes]]"" of ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"" has no attribute ""mask"" [union-attr] xarray/plot/facetgrid.py:684: error: ""FigureCanvasBase"" has no attribute ""get_renderer"" [attr-defined] xarray/plot/accessor.py:182: error: Overloaded function signatures 1 and 2 overlap with incompatible return types [misc] xarray/plot/accessor.py:182: error: Overloaded function signatures 1 and 3 overlap with incompatible return types [misc] xarray/plot/accessor.py:309: error: Overloaded function signatures 1 and 2 overlap with incompatible return types [misc] xarray/plot/accessor.py:309: error: Overloaded function signatures 1 and 3 overlap with incompatible return types [misc] xarray/plot/accessor.py:428: error: Overloaded function implementation cannot produce return type of signature 2 [misc] xarray/plot/accessor.py:428: error: Overloaded function implementation cannot produce return type of signature 3 [misc] xarray/plot/accessor.py:433: error: Overloaded function signatures 1 and 2 overlap with incompatible return types [misc] xarray/plot/accessor.py:433: error: Overloaded function signatures 1 and 3 overlap with incompatible return types [misc] xarray/plot/accessor.py:552: error: Overloaded function implementation cannot produce return type of signature 2 [misc] xarray/plot/accessor.py:552: error: Overloaded function implementation cannot produce return type of signature 3 [misc] xarray/plot/accessor.py:557: error: Overloaded function signatures 1 and 2 overlap with incompatible return types [misc] xarray/plot/accessor.py:557: error: Overloaded function signatures 1 and 3 overlap with incompatible return types [misc] xarray/plot/accessor.py:676: error: Overloaded function implementation cannot produce return type of signature 2 [misc] xarray/plot/accessor.py:676: error: Overloaded function implementation cannot produce return type of signature 3 [misc] xarray/plot/accessor.py:681: error: Overloaded function signatures 1 and 2 overlap with incompatible return types [misc] xarray/plot/accessor.py:681: error: Overloaded function signatures 1 and 3 overlap with incompatible return types [misc] xarray/plot/accessor.py:800: error: Overloaded function implementation cannot produce return type of signature 2 [misc] xarray/plot/accessor.py:800: error: Overloaded function implementation cannot produce return type of signature 3 [misc] xarray/plot/accessor.py:948: error: Overloaded function signatures 1 and 2 overlap with incompatible return types [misc] xarray/plot/accessor.py:948: error: Overloaded function signatures 1 and 3 overlap with incompatible return types [misc] xarray/plot/accessor.py:1075: error: Overloaded function signatures 1 and 2 overlap with incompatible return types [misc] xarray/plot/accessor.py:1075: error: Overloaded function signatures 1 and 3 overlap with incompatible return types [misc] xarray/plot/accessor.py:1190: error: Overloaded function signatures 1 and 2 overlap with incompatible return types [misc] xarray/plot/accessor.py:1190: error: Overloaded function signatures 1 and 3 overlap with incompatible return types [misc] xarray/plot/dataset_plot.py:324: error: Overloaded function signatures 1 and 2 overlap with incompatible return types [misc] xarray/plot/dataset_plot.py:324: error: Overloaded function signatures 1 and 3 overlap with incompatible return types [misc] xarray/plot/dataset_plot.py:478: error: Overloaded function signatures 1 and 2 overlap with incompatible return types [misc] xarray/plot/dataset_plot.py:478: error: Overloaded function signatures 1 and 3 overlap with incompatible return types [misc] xarray/plot/dataset_plot.py:649: error: Function gets multiple values for keyword argument ""x"" [misc] xarray/plot/dataset_plot.py:649: error: Function gets multiple values for keyword argument ""y"" [misc] xarray/plot/dataset_plot.py:649: error: Function gets multiple values for keyword argument ""u"" [misc] xarray/plot/dataset_plot.py:649: error: Function gets multiple values for keyword argument ""v"" [misc] xarray/plot/dataset_plot.py:649: error: Function gets multiple values for keyword argument ""density"" [misc] xarray/plot/dataset_plot.py:649: error: Function gets multiple values for keyword argument ""linewidth"" [misc] xarray/plot/dataset_plot.py:649: error: Function gets multiple values for keyword argument ""color"" [misc] xarray/plot/dataset_plot.py:649: error: Function gets multiple values for keyword argument ""cmap"" [misc] xarray/plot/dataset_plot.py:649: error: Function gets multiple values for keyword argument ""norm"" [misc] xarray/plot/dataset_plot.py:649: error: Function gets multiple values for keyword argument ""arrowsize"" [misc] xarray/plot/dataset_plot.py:649: error: Function gets multiple values for keyword argument ""arrowstyle"" [misc] xarray/plot/dataset_plot.py:649: error: Function gets multiple values for keyword argument ""minlength"" [misc] xarray/plot/dataset_plot.py:649: error: Function gets multiple values for keyword argument ""transform"" [misc] xarray/plot/dataset_plot.py:649: error: Function gets multiple values for keyword argument ""zorder"" [misc] xarray/plot/dataset_plot.py:649: error: Function gets multiple values for keyword argument ""start_points"" [misc] xarray/plot/dataset_plot.py:649: error: Function gets multiple values for keyword argument ""maxlength"" [misc] xarray/plot/dataset_plot.py:649: error: Function gets multiple values for keyword argument ""integration_direction"" [misc] xarray/plot/dataset_plot.py:649: error: Function gets multiple values for keyword argument ""broken_streamlines"" [misc] xarray/plot/dataset_plot.py:649: error: Argument 1 has incompatible type ""*List[ndarray[Any, Any]]""; expected ""Union[float, Tuple[float, float]]"" [arg-type] xarray/plot/dataset_plot.py:649: error: Argument 1 has incompatible type ""*List[ndarray[Any, Any]]""; expected ""Union[str, Colormap, None]"" [arg-type] xarray/plot/dataset_plot.py:649: error: Argument 1 has incompatible type ""*List[ndarray[Any, Any]]""; expected ""Union[str, Normalize, None]"" [arg-type] xarray/plot/dataset_plot.py:649: error: Argument 1 has incompatible type ""*List[ndarray[Any, Any]]""; expected ""float"" [arg-type] xarray/plot/dataset_plot.py:649: error: Argument 1 has incompatible type ""*List[ndarray[Any, Any]]""; expected ""Union[str, ArrowStyle]"" [arg-type] xarray/plot/dataset_plot.py:649: error: Argument 1 has incompatible type ""*List[ndarray[Any, Any]]""; expected ""Optional[Transform]"" [arg-type] xarray/plot/dataset_plot.py:649: error: Argument 1 has incompatible type ""*List[ndarray[Any, Any]]""; expected ""Optional[float]"" [arg-type] xarray/plot/dataset_plot.py:649: error: Argument 1 has incompatible type ""*List[ndarray[Any, Any]]""; expected ""Literal['forward', 'backward', 'both']"" [arg-type] xarray/plot/dataset_plot.py:649: error: Argument 1 has incompatible type ""*List[ndarray[Any, Any]]""; expected ""bool"" [arg-type] xarray/plot/dataset_plot.py:751: error: Overloaded function signatures 1 and 2 overlap with incompatible return types [misc] xarray/plot/dataset_plot.py:751: error: Overloaded function signatures 1 and 3 overlap with incompatible return types [misc] xarray/plot/dataarray_plot.py:718: error: Incompatible return value type (got ""Tuple[Union[ndarray[Any, Any], List[ndarray[Any, Any]]], ndarray[Any, Any], Union[BarContainer, Polygon, List[Union[BarContainer, Polygon]]]]"", expected ""Tuple[ndarray[Any, Any], ndarray[Any, Any], BarContainer]"") [return-value] xarray/plot/dataarray_plot.py:996: error: ""Axes"" has no attribute ""view_init"" [attr-defined] xarray/plot/dataarray_plot.py:1106: error: Overloaded function signatures 1 and 2 overlap with incompatible return types [misc] xarray/plot/dataarray_plot.py:1106: error: Overloaded function signatures 1 and 3 overlap with incompatible return types [misc] xarray/plot/dataarray_plot.py:1261: error: Argument 1 to ""scatter"" of ""Axes"" has incompatible type ""*List[ndarray[Any, Any]]""; expected ""Union[Sequence[Union[Union[Tuple[float, float, float], str], Union[str, Tuple[float, float, float, float], Tuple[Union[Tuple[float, float, float], str], float], Tuple[Tuple[float, float, float, float], float]]]], Union[Union[Tuple[float, float, float], str], Union[str, Tuple[float, float, float, float], Tuple[Union[Tuple[float, float, float], str], float], Tuple[Tuple[float, float, float, float], float]]], None]"" [arg-type] xarray/plot/dataarray_plot.py:1261: error: Argument 1 to ""scatter"" of ""Axes"" has incompatible type ""*List[ndarray[Any, Any]]""; expected ""Optional[Union[str, Path, MarkerStyle]]"" [arg-type] xarray/plot/dataarray_plot.py:1261: error: Argument 1 to ""scatter"" of ""Axes"" has incompatible type ""*List[ndarray[Any, Any]]""; expected ""Union[str, Colormap, None]"" [arg-type] xarray/plot/dataarray_plot.py:1261: error: Argument 1 to ""scatter"" of ""Axes"" has incompatible type ""*List[ndarray[Any, Any]]""; expected ""Union[str, Normalize, None]"" [arg-type] xarray/plot/dataarray_plot.py:1261: error: Argument 1 to ""scatter"" of ""Axes"" has incompatible type ""*List[ndarray[Any, Any]]""; expected ""Optional[float]"" [arg-type] xarray/plot/dataarray_plot.py:1261: error: Argument 1 to ""scatter"" of ""Axes"" has incompatible type ""*List[ndarray[Any, Any]]""; expected ""Union[float, Sequence[float], None]"" [arg-type] xarray/plot/dataarray_plot.py:1615: error: ""Axes"" has no attribute ""set_zlabel"" [attr-defined] xarray/plot/dataarray_plot.py:1655: error: Overloaded function signatures 1 and 2 overlap with incompatible return types [misc] xarray/plot/dataarray_plot.py:1655: error: Overloaded function signatures 1 and 3 overlap with incompatible return types [misc] xarray/plot/dataarray_plot.py:1874: error: Overloaded function signatures 1 and 2 overlap with incompatible return types [misc] xarray/plot/dataarray_plot.py:1874: error: Overloaded function signatures 1 and 3 overlap with incompatible return types [misc] xarray/plot/dataarray_plot.py:2010: error: Overloaded function signatures 1 and 2 overlap with incompatible return types [misc] xarray/plot/dataarray_plot.py:2010: error: Overloaded function signatures 1 and 3 overlap with incompatible return types [misc] xarray/plot/dataarray_plot.py:2146: error: Overloaded function signatures 1 and 2 overlap with incompatible return types [misc] xarray/plot/dataarray_plot.py:2146: error: Overloaded function signatures 1 and 3 overlap with incompatible return types [misc] xarray/plot/dataarray_plot.py:2464: error: ""Axes"" has no attribute ""plot_surface"" [attr-defined] xarray/tests/test_plot.py:427: error: Value of type ""Union[_SupportsArray[dtype[Any]], _NestedSequence[_SupportsArray[dtype[Any]]], int, float, complex, str, bytes, _NestedSequence[Union[bool, int, float, complex, str, bytes]]]"" is not indexable [index] xarray/tests/test_plot.py:443: error: Module has no attribute ""viridis"" [attr-defined] xarray/tests/test_plot.py:457: error: ""None"" not callable [misc] xarray/tests/test_plot.py:462: error: ""None"" not callable [misc] xarray/tests/test_plot.py:465: error: ""None"" not callable [misc] xarray/tests/test_plot.py:471: error: Module has no attribute ""viridis"" [attr-defined] xarray/tests/test_plot.py:477: error: Module has no attribute ""viridis"" [attr-defined] xarray/tests/test_plot.py:482: error: Module has no attribute ""viridis"" [attr-defined] xarray/tests/test_plot.py:486: error: Module has no attribute ""viridis"" [attr-defined] xarray/tests/test_plot.py:493: error: ""None"" not callable [misc] xarray/tests/test_plot.py:498: error: ""None"" not callable [misc] xarray/tests/test_plot.py:501: error: ""None"" not callable [misc] xarray/tests/test_plot.py:931: error: Module has no attribute ""magma"" [attr-defined] xarray/tests/test_plot.py:933: error: Module has no attribute ""magma"" [attr-defined] xarray/tests/test_plot.py:1173: error: Module has no attribute ""RdBu"" [attr-defined] xarray/tests/test_plot.py:1746: error: Item ""Colormap"" of ""Optional[Colormap]"" has no attribute ""colors"" [union-attr] xarray/tests/test_plot.py:1746: error: Item ""None"" of ""Optional[Colormap]"" has no attribute ""colors"" [union-attr] xarray/tests/test_plot.py:1747: error: Item ""Colormap"" of ""Optional[Colormap]"" has no attribute ""colors"" [union-attr] xarray/tests/test_plot.py:1747: error: Item ""None"" of ""Optional[Colormap]"" has no attribute ""colors"" [union-attr] xarray/tests/test_plot.py:1749: error: Item ""Colormap"" of ""Optional[Colormap]"" has no attribute ""_rgba_over"" [union-attr] xarray/tests/test_plot.py:1749: error: Item ""None"" of ""Optional[Colormap]"" has no attribute ""_rgba_over"" [union-attr] xarray/tests/test_plot.py:1801: error: Item ""None"" of ""Optional[ndarray[Any, Any]]"" has no attribute ""size"" [union-attr] xarray/tests/test_plot.py:1952: error: Item ""None"" of ""Optional[ndarray[Any, Any]]"" has no attribute ""min"" [union-attr] xarray/tests/test_plot.py:1952: error: Item ""None"" of ""Optional[ndarray[Any, Any]]"" has no attribute ""max"" [union-attr] xarray/tests/test_plot.py:1968: error: Item ""None"" of ""Optional[ndarray[Any, Any]]"" has no attribute ""dtype"" [union-attr] xarray/tests/test_plot.py:1969: error: Value of type ""Optional[ndarray[Any, Any]]"" is not indexable [index] xarray/tests/test_plot.py:2125: error: ""Artist"" has no attribute ""get_clim"" [attr-defined] xarray/tests/test_plot.py:2135: error: ""Colorbar"" has no attribute ""vmin"" [attr-defined] xarray/tests/test_plot.py:2136: error: ""Colorbar"" has no attribute ""vmax"" [attr-defined] xarray/tests/test_plot.py:2202: error: ""Artist"" has no attribute ""get_clim"" [attr-defined] xarray/tests/test_plot.py:2218: error: ""Artist"" has no attribute ""norm"" [attr-defined] xarray/tests/test_plot.py:2747: error: Item ""_AxesBase"" of ""Optional[_AxesBase]"" has no attribute ""legend_"" [union-attr] xarray/tests/test_plot.py:2747: error: Item ""None"" of ""Optional[_AxesBase]"" has no attribute ""legend_"" [union-attr] xarray/tests/test_plot.py:2754: error: Item ""None"" of ""Optional[_AxesBase]"" has no attribute ""get_legend"" [union-attr] xarray/tests/test_plot.py:2775: error: Item ""None"" of ""Optional[FigureBase]"" has no attribute ""axes"" [union-attr] xarray/tests/test_plot.py:2775: error: Argument 1 to ""len"" has incompatible type ""Union[_AxesBase, None, Any]""; expected ""Sized"" [arg-type] xarray/tests/test_plot.py:2803: error: Module has no attribute ""dates"" [attr-defined] xarray/tests/test_plot.py:2812: error: Module has no attribute ""dates"" [attr-defined] xarray/tests/test_plot.py:2831: error: Item ""None"" of ""Optional[_AxesBase]"" has no attribute ""xaxis"" [union-attr] xarray/tests/test_plot.py:2831: error: Module has no attribute ""dates"" [attr-defined] xarray/tests/test_groupby.py:715: error: Argument 1 to ""groupby"" of ""Dataset"" has incompatible type ""ndarray[Any, dtype[signedinteger[Any]]]""; expected ""Union[Hashable, DataArray, IndexVariable]"" [arg-type] xarray/tests/test_groupby.py:715: note: Following member(s) of ""ndarray[Any, dtype[signedinteger[Any]]]"" have conflicts: xarray/tests/test_groupby.py:715: note: __hash__: expected ""Callable[[], int]"", got ""None"" xarray/tests/test_dataset.py:6964: error: ""PlainQuantity[Any]"" not callable [operator] xarray/tests/test_dataset.py:6965: error: ""PlainQuantity[Any]"" not callable [operator] xarray/tests/test_dataset.py:7007: error: ""PlainQuantity[Any]"" not callable [operator] xarray/tests/test_dataset.py:7008: error: ""PlainQuantity[Any]"" not callable [operator] xarray/tests/test_dataarray.py:6687: error: ""PlainQuantity[Any]"" not callable [operator] xarray/tests/test_dataarray.py:6689: error: ""PlainQuantity[Any]"" not callable [operator] xarray/tests/test_dataarray.py:6735: error: ""PlainQuantity[Any]"" not callable [operator] xarray/tests/test_dataarray.py:6737: error: ""PlainQuantity[Any]"" not callable [operator] ```
Some guidance how to solve these: > - `[xy]ticks` in mpl is currently overly narrowly type hinted because I was following the docstring, but I agree that `ArrayLike` is a better type hint for that, plan on updating (including the docstring) upstream > - `[xy]lim` originally neglected the case of passing `set_xlim((min, max))` as a tuple, but that has been updated. xarray has that type hinted as array like, but mpl has it hinted as a 2-tuple (I think it is currently still of floats, but may be expanded as we more directly address units/categoricals/etc). Willing to debate here, but my starting position is that the ""exactly 2 values"" is valuable info here, and I think `tuple` is the only way to do that. > - `get_renderer` is not actually available on all of our backends, we should maybe see if there is a more preferred way of doing what you are doing here that will work for all backends, but haven't looked into it too closely. > - `Module has no attribute ` is another instance of dynamically generated behavior which can't be statically type checked (elegantly, at least), can probably be replaced by `mpl.colormaps[""""]` in many cases, which is statically typecheckable > - Anything to do with 3D Axes is not type hinted, perhaps ignore for now (or help us get that type hinted adequately, but it is relatively low priority currently) > - `Module has no attribute ""dates""` we don't currently type hint dates/units things, but it is on my mind, not sure yet if it will be in first release or not though (may at least put a placeholder that gets rid of this error, but treats everything as ""Any""). _Originally posted by @ksunden in https://github.com/pydata/xarray/issues/7787#issuecomment-1523743471_ > The suggestion from mpl (specifically @tacaswell) was to use [constrained layout](https://matplotlib.org/stable/tutorials/intermediate/constrainedlayout_guide.html) for the purpose that xarray currently uses `get_renderer`, this will ensure that the `facetgrid` works with all mpl backends. _Originally posted by @ksunden in https://github.com/pydata/xarray/issues/7787#issuecomment-1528091492_ > I'm also relatively sure that if you are willing to put a floor on the version of Matplotlib you support `get_window_extent` will use it's internally cached renderer (and when we make it uniformly optional we also fixed the cache invalidation logic). _Originally posted by @tacaswell in https://github.com/pydata/xarray/issues/7787#issuecomment-1528096647_ ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/7802/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 1826978659,I_kwDOAMm_X85s5Xtj,8028,Setting datarrays with non-dimension coordinates errors,14371165,open,0,,,6,2023-07-28T19:20:31Z,2023-08-10T15:25:23Z,,MEMBER,,,,"### What happened? I'm not sure if this is a bug or a feature but I was expecting this example to work since the new coord is just a slight rewrite of the original dimension coordinate: ```python import xarray as xr ds = xr.tutorial.open_dataset(""air_temperature"") # Change the first time value: ds[""air_new""] = ds.air.copy() air_new_changed = ds.air_new[{""time"": 0}] * 3 ds.air_new.loc[air_new_changed.coords] = air_new_changed # Works! :) # Add a another coord along time axis and change # the first time value: ds[""air_new""] = ds.air.copy().assign_coords( {""time_float"": ds.time.astype(float)} ) air_new_changed = ds.air_new[{""time"": 0}] * 4 ds.air_new.loc[air_new_changed.coords] = air_new_changed # Error! :( Traceback (most recent call last): Cell In[25], line 5 ds.air_new.loc[air_new_changed.coords] = air_new_changed File ~\AppData\Local\mambaforge\envs\jw\lib\site-packages\xarray\core\dataarray.py:222 in __setitem__ dim_indexers = map_index_queries(self.data_array, key).dim_indexers File ~\AppData\Local\mambaforge\envs\jw\lib\site-packages\xarray\core\indexing.py:182 in map_index_queries grouped_indexers = group_indexers_by_index(obj, indexers, options) File ~\AppData\Local\mambaforge\envs\jw\lib\site-packages\xarray\core\indexing.py:144 in group_indexers_by_index raise KeyError(f""no index found for coordinate {key!r}"") KeyError: ""no index found for coordinate 'time_float'"" ``` ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/8028/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue 1795519181,I_kwDOAMm_X85rBXLN,7969,Upstream CI is failing,14371165,closed,0,,,2,2023-07-09T18:51:41Z,2023-07-10T17:34:12Z,2023-07-10T17:33:12Z,MEMBER,,,,"### What happened? The upstream CI has been failing for a while. Here's the latest: https://github.com/pydata/xarray/actions/runs/5501368493/jobs/10024902009#step:7:16 ```python Traceback (most recent call last): File """", line 1, in File ""/home/runner/work/xarray/xarray/xarray/__init__.py"", line 1, in from xarray import testing, tutorial File ""/home/runner/work/xarray/xarray/xarray/testing.py"", line 7, in import numpy as np ModuleNotFoundError: No module named 'numpy' ``` Digging a little in the logs ``` Installing build dependencies: started Installing build dependencies: finished with status 'error' error: subprocess-exited-with-error × pip subprocess to install build dependencies did not run successfully. │ exit code: 1 ╰─> [3 lines of output] Looking in indexes: https://pypi.anaconda.org/scipy-wheels-nightly/simple ERROR: Could not find a version that satisfies the requirement meson-python==0.13.1 (from versions: none) ERROR: No matching distribution found for meson-python==0.13.1 [end of output] ``` Might be some numpy problem? Should the CI be robust enough to handle these kinds of errors? Because I suppose we would like to get the automatic issue created anyway?","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/7969/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 1125040125,I_kwDOAMm_X85DDr_9,6244,Get pyupgrade to update the typing,14371165,closed,0,,,2,2022-02-05T21:56:56Z,2023-03-12T15:38:37Z,2023-03-12T15:38:37Z,MEMBER,,,,"### Is your feature request related to a problem? Use more up-to-date typing styles on all files. Will reduce number of imports and avoids big diffs when doing relatively minor changes because pre-commit/pyupgrade has been triggered somehow. Related to #6240 ### Describe the solution you'd like Add `from __future__ import annotations` on files with a lot of typing. Let pyupgrade do the rest. ### Describe alternatives you've considered _No response_ ### Additional context _No response_","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/6244/reactions"", ""total_count"": 3, ""+1"": 3, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 1537068105,I_kwDOAMm_X85bncxJ,7450,Backend array documentation typo,14371165,open,0,,,0,2023-01-17T21:37:26Z,2023-01-17T21:56:12Z,,MEMBER,,,,"### What happened? https://docs.xarray.dev/en/stable/internals/how-to-add-new-backend.html#indexing-examples I believe there's a typo in the BASIC indexing support example: ```python # shall support integers backend_array._raw_indexing_method(1, 1) ``` Should be: ```python # shall support integers backend_array._raw_indexing_method((1, 1)) ``` Suggestion of possible fixes: * Make sure it is a typo. * Create a valid custom MyBackendArray and initialize it. So it is easier to tell if it's a typo. * Add type hinting so mypy can easier catch these errors. ### What did you expect to happen? _No response_ ### Minimal Complete Verifiable Example _No response_ ### MVCE confirmation - [ ] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray. - [ ] Complete example — the example is self-contained, including all data and the text of any traceback. - [ ] Verifiable example — the example copy & pastes into an IPython prompt or [Binder notebook](https://mybinder.org/v2/gh/pydata/xarray/main?urlpath=lab/tree/doc/examples/blank_template.ipynb), returning the result. - [ ] New issue — a search of GitHub Issues suggests this is not a duplicate. ### Relevant log output _No response_ ### Anything else we need to know? _No response_ ### Environment
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/7450/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue 1318800553,I_kwDOAMm_X85Om0yp,6833,Require a pull request before merging to main,14371165,closed,0,,,4,2022-07-26T22:09:55Z,2023-01-13T16:51:03Z,2023-01-13T16:51:03Z,MEMBER,,,,"### Is your feature request related to a problem? I was making sure the test in #6832 failed on main, when it did I wrote a few lines in the `what's new` file but forgot switching back to the other branch and accidentally pushed directly to main. :( ### Describe the solution you'd like I think it's best if we require a pull request for merging. We seem to pretty much do this anyway. Seems to be this setting if I understand correctly: ![image](https://user-images.githubusercontent.com/14371165/181120776-dd2dc5c8-6467-41c3-8edf-c332151355cc.png) ### Describe alternatives you've considered _No response_ ### Additional context _No response_","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/6833/reactions"", ""total_count"": 3, ""+1"": 3, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 1376776178,I_kwDOAMm_X85SD-_y,7049,Backend entrypoints not public?,14371165,closed,0,,,0,2022-09-17T13:41:13Z,2022-10-26T16:01:06Z,2022-10-26T16:01:06Z,MEMBER,,,,"### What is your issue? As I've understood it `ZarrBackendEntrypoint` is the engine used when loading zarr-files. But for some reason we show `ZarrStore` in `xr.backends`. I believe the `ZarrStore` class is supposed to be just a implementation detail, right? ```python # The available engines: xr.backends.list_engines() Out[23]: {'netcdf4': , 'h5netcdf': , 'scipy': , 'pseudonetcdf': , 'pydap': , 'store': , 'zarr': } # The public class is ZarrStore instead of ZarrBackendEntrypoint, how come? dir(xr.backends) Out[22]: ['AbstractDataStore', 'BackendArray', 'BackendEntrypoint', 'CachingFileManager', 'CfGribDataStore', 'DummyFileManager', 'FileManager', 'H5NetCDFStore', 'InMemoryDataStore', 'NetCDF4DataStore', 'NioDataStore', 'PseudoNetCDFDataStore', 'PydapDataStore', 'ScipyDataStore', 'ZarrStore', '__all__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__path__', '__spec__', 'api', 'cfgrib_', 'common', 'file_manager', 'h5netcdf_', 'list_engines', 'locks', 'lru_cache', 'memory', 'netCDF4_', 'netcdf3', 'plugins', 'pseudonetcdf_', 'pydap_', 'pynio_', 'rasterio_', 'scipy_', 'store', 'zarr'] ```","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/7049/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 1410534774,I_kwDOAMm_X85UEw12,7170,Scatter plots overlap in facetgrid in 3d,14371165,open,0,,,0,2022-10-16T16:06:56Z,2022-10-16T16:08:55Z,,MEMBER,,,,"### What happened? Any matplotlib gurus have any ideas how to nicely fit 3d plots in facetgrid? ```python ds = xr.tutorial.scatter_example_dataset(seed=42) fg = ds.plot.scatter(x=""A"", y=""B"", z=""z"", hue=""y"", markersize=""x"", row=""x"", col=""w"") ``` ![image](https://user-images.githubusercontent.com/14371165/196045673-ad6322d4-63f4-4f94-aca6-a4cfdc682fea.png) 2d looks fine: ```python fg = ds.plot.scatter(x=""A"", y=""B"", hue=""y"", markersize=""x"", row=""x"", col=""w"") ``` ![image](https://user-images.githubusercontent.com/14371165/196045774-5d850317-c58a-4fac-ae4b-04660a140fd1.png) ### What did you expect to happen? No plots overlapping each other, even if rotating the plots. ### Minimal Complete Verifiable Example _No response_ ### MVCE confirmation - [x] Minimal example — the example is as focused as reasonably possible to demonstrate the underlying issue in xarray. - [x] Complete example — the example is self-contained, including all data and the text of any traceback. - [x] Verifiable example — the example copy & pastes into an IPython prompt or [Binder notebook](https://mybinder.org/v2/gh/pydata/xarray/main?urlpath=lab/tree/doc/examples/blank_template.ipynb), returning the result. - [x] New issue — a search of GitHub Issues suggests this is not a duplicate. ### Relevant log output _No response_ ### Anything else we need to know? _No response_ ### Environment
xr.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 3.10.6 | packaged by conda-forge | (main, Aug 22 2022, 20:30:19) [MSC v.1929 64 bit (AMD64)] python-bits: 64 OS: Windows OS-release: 10 machine: AMD64 processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel byteorder: little LC_ALL: None LANG: en LOCALE: ('Swedish_Sweden', '1252') libhdf5: 1.12.2 libnetcdf: 4.8.1 xarray: 2022.9.1.dev266+gbd01f9cc.d20221006 pandas: 1.5.0 numpy: 1.23.3 scipy: 1.9.1 netCDF4: 1.6.1 pydap: installed h5netcdf: 1.0.2 h5py: 3.7.0 Nio: None zarr: 2.13.2 cftime: 1.6.2 nc_time_axis: 1.4.1 PseudoNetCDF: 3.2.2 rasterio: 1.3.2 cfgrib: None iris: 3.3.0 bottleneck: 1.3.5 dask: 2022.9.2 distributed: 2022.9.2 matplotlib: 3.6.0 cartopy: 0.21.0 seaborn: 0.12.0 numbagg: 0.2.1 fsspec: 2022.8.2 cupy: None pint: 0.19.2 sparse: 0.13.0 flox: 0.5.10.dev21+g91b6e19 numpy_groupies: 0.9.19 setuptools: 65.4.1 pip: 22.2.2 conda: None pytest: 7.1.3 IPython: 7.33.0 sphinx: 5.2.3 C:\Users\J.W\anaconda3\envs\xarray-tests\lib\site-packages\_distutils_hack\__init__.py:33: UserWarning: Setuptools is replacing distutils. warnings.warn(""Setuptools is replacing distutils."")
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/7170/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue 1377088142,I_kwDOAMm_X85SFLKO,7050,Type annotation guidelines,14371165,open,0,,,2,2022-09-18T15:04:54Z,2022-09-23T01:55:19Z,,MEMBER,,,,"Dask has a pretty nice guideline for type hinting, see https://github.com/dask/community/issues/255. Notable for us is to avoid adding typing in docstrings to avoid duplicating information.","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/7050/reactions"", ""total_count"": 4, ""+1"": 4, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue 1046458609,I_kwDOAMm_X84-X7Dx,5945,Start using `|` instead of `Union` or `Optional` when typing,14371165,closed,0,,,1,2021-11-06T08:12:57Z,2022-06-04T04:26:03Z,2022-06-04T04:26:03Z,MEMBER,,,," **Is your feature request related to a problem? Please describe.** To make it easier reading the typing it is now possible to use `|` instead of `Union` or `Optional`. Here's an example how it looks like in pandas: https://github.com/pandas-dev/pandas/blob/master/pandas/plotting/_core.py#L116-L134 **Describe the solution you'd like** Replace for example: * `Union[str, int]` with `str | int` * `Optional[str]` with `None | str` This would likely require adding `from __future__ import annotations` at the top of the file. References https://www.python.org/dev/peps/pep-0604/ ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5945/reactions"", ""total_count"": 2, ""+1"": 2, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 597785475,MDU6SXNzdWU1OTc3ODU0NzU=,3962,"Interpolation - Support extrapolation method ""clip""",14371165,open,0,,,4,2020-04-10T09:07:13Z,2022-05-02T13:42:24Z,,MEMBER,,,,"Hello, I would like an option in `da.interp() `that instead of returning NaNs during extrapolation returns the data corresponding to the end of the breakpoint data set range. One way to do this is to limit the new coordinates to the array coordinates minimum and maximum value, I did a simple example with this solution down below. I think this is a rather safe way as we are just modifying the inputs to all the various interpolation classes that xarray is using at the moment. But it does look a little weird when printing the extrapolated value, the coordinates shows the limited value instead of the requested coordinates. Maybe this can be handled elegantly somewhere in the source code? MATLAB uses this quite frequently in their interpolation functions: * https://mathworks.com/help/simulink/ug/methods-for-estimating-missing-points.html * https://mathworks.com/help/simulink/slref/2dlookuptable.html #### MCVE Code Sample ```python import numpy as np import xarray as xr def interp(da, coords, extrapolation='clip'): """""" Linear interpolation that clips the inputs to the coords min and max value. Parameters ---------- da : DataArray DataArray to interpolate. coords : dict Coordinates for the interpolated value. """""" if extrapolation == 'clip': for k, v in da.coords.items(): coords[k] = np.maximum(coords[k], np.min(v.values)) coords[k] = np.minimum(coords[k], np.max(v.values)) return da.interp(coords) # Create coordinates: x = np.linspace(1000, 6000, 4) y = np.linspace(100, 1200, 3) # Create data: X = np.meshgrid(*[x, y], indexing='ij') data = X[0] * X[1] # Create DataArray: da = xr.DataArray(data=data, coords=[('x', x), ('y', y)], name='data') # Attempt to extrapolate: datai = interp(da, {'x': 7000, 'y': 375}) ``` #### Expected Output ````python print(datai) array(2250000.) Coordinates: x float64 6e+03 y float64 375.0 ```` #### Versions
Output of `xr.show_versions()` INSTALLED VERSIONS ------------------ commit: None python: 3.7.7 (default, Mar 23 2020, 23:19:08) [MSC v.1916 64 bit (AMD64)] python-bits: 64 OS: Windows OS-release: 10 machine: AMD64 processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel byteorder: little LC_ALL: None LANG: en LOCALE: None.None libhdf5: 1.10.4 libnetcdf: None xarray: 0.15.0 pandas: 1.0.3 numpy: 1.18.1 scipy: 1.4.1 netCDF4: None pydap: None h5netcdf: None h5py: 2.10.0 Nio: None zarr: None cftime: None nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: 1.3.2 dask: 2.13.0 distributed: 2.13.0 matplotlib: 3.1.3 cartopy: None seaborn: 0.10.0 numbagg: None setuptools: 46.1.3.post20200330 pip: 20.0.2 conda: 4.8.3 pytest: 5.4.1 IPython: 7.13.0 sphinx: 2.4.4
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/3962/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue 1182697604,I_kwDOAMm_X85GfoiE,6416,xr.concat removes datetime information,14371165,closed,0,,,2,2022-03-27T23:19:30Z,2022-03-28T16:05:01Z,2022-03-28T16:05:01Z,MEMBER,,,,"### What happened? xr.concat removes datetime information and can't concatenate the arrays because they don't have compatible types anymore. ### What did you expect to happen? Succesful concatenation with the same type. ### Minimal Complete Verifiable Example ```Python import numpy as np import xarray as xr from datetime import datetime month = np.arange(1, 13, 1) data = np.sin(2 * np.pi * month / 12.0) darray = xr.DataArray(data, dims=[""time""]) darray.coords[""time""] = np.array([datetime(2017, m, 1) for m in month]) darray_nan = np.nan * darray.isel(**{""time"": -1}) darray = xr.concat([darray, darray_nan], dim=""time"") ``` ### Relevant log output ```Python Traceback (most recent call last): File """", line 2, in darray = xr.concat([darray, darray_nan], dim=""time"") File ""c:\users\j.w\documents\github\xarray\xarray\core\concat.py"", line 244, in concat return f( File ""c:\users\j.w\documents\github\xarray\xarray\core\concat.py"", line 642, in _dataarray_concat ds = _dataset_concat( File ""c:\users\j.w\documents\github\xarray\xarray\core\concat.py"", line 555, in _dataset_concat combined_idx = indexes[0].concat(indexes, dim, positions) File ""c:\users\j.w\documents\github\xarray\xarray\core\indexes.py"", line 318, in concat coord_dtype = np.result_type(*[idx.coord_dtype for idx in indexes]) File ""<__array_function__ internals>"", line 5, in result_type TypeError: The DType could not be promoted by . This means that no common DType exists for the given inputs. For example they cannot be stored in a single array unless the dtype is `object`. The full list of DTypes is: (, ) ``` ### Anything else we need to know? Similar to #6384. Happens around here: https://github.com/pydata/xarray/blob/728b648d5c7c3e22fe3704ba163012840408bf66/xarray/core/concat.py#L535 ### Environment
INSTALLED VERSIONS ------------------ commit: None python: 3.9.6 | packaged by conda-forge | (default, Jul 11 2021, 03:37:25) [MSC v.1916 64 bit (AMD64)] python-bits: 64 OS: Windows OS-release: 10 machine: AMD64 processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel byteorder: little LC_ALL: None LANG: en LOCALE: ('Swedish_Sweden', '1252') libhdf5: 1.10.6 libnetcdf: 4.7.4 xarray: 0.16.3.dev99+gc19467fb pandas: 1.3.1 numpy: 1.21.5 scipy: 1.7.1 netCDF4: 1.5.6 pydap: installed h5netcdf: 0.11.0 h5py: 2.10.0 Nio: None zarr: 2.8.3 cftime: 1.5.0 nc_time_axis: 1.3.1 PseudoNetCDF: installed rasterio: 1.2.6 cfgrib: None iris: 3.0.4 bottleneck: 1.3.2 dask: 2021.10.0 distributed: 2021.10.0 matplotlib: 3.4.3 cartopy: 0.19.0.post1 seaborn: 0.11.1 numbagg: 0.2.1 fsspec: 2021.11.1 cupy: None pint: 0.17 sparse: 0.12.0 setuptools: 49.6.0.post20210108 pip: 21.2.4 conda: None pytest: 6.2.4 IPython: 7.31.0 sphinx: 4.3.2
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/6416/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 1174585854,I_kwDOAMm_X85GAsH-,6384,xr.concat adds an extra array around elements,14371165,closed,0,,,1,2022-03-20T15:25:49Z,2022-03-21T04:49:23Z,2022-03-21T04:49:23Z,MEMBER,,,,"### What happened? When concatenating dataarrays with `pd.Interval` along a dim the `pd.Interval` is wrapped with a numpy array and appended instead of without like it it was before #5692. ### Minimal Complete Verifiable Example ```Python import numpy as np import xarray as xr shape = (2, 3, 4) darray = xr.DataArray(np.linspace(0, 1, num=np.prod(shape)).reshape(shape)) bins = [-1, 0, 1, 2] a = darray.groupby_bins(""dim_0"", bins).mean(...) a_nan = np.nan * a.isel(**{""dim_0_bins"": -1}) out = xr.concat([a, a_nan], dim=""dim_0_bins"") print(out[""dim_0_bins""]) ``` ### Relevant log output Current result: ```Python array([Interval(-1, 0, closed='right'), Interval(0, 1, closed='right'), Interval(1, 2, closed='right'), array(Interval(1, 2, closed='right'), dtype=object)], dtype=object) Coordinates: * dim_0_bins (dim_0_bins) object (-1, 0] (0, 1] (1, 2] (1, 2] ``` Should be: ```python array([Interval(-1, 0, closed='right'), Interval(0, 1, closed='right'), Interval(1, 2, closed='right'), Interval(1, 2, closed='right')], dtype=object) Coordinates: * dim_0_bins (dim_0_bins) object (-1, 0] (0, 1] (1, 2] (1, 2] ``` ### Anything else we need to know? _No response_ ### Environment
xr.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 3.9.6 | packaged by conda-forge | (default, Jul 11 2021, 03:37:25) [MSC v.1916 64 bit (AMD64)] python-bits: 64 OS: Windows OS-release: 10 machine: AMD64 processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel byteorder: little LC_ALL: None LANG: en LOCALE: ('Swedish_Sweden', '1252') libhdf5: 1.10.6 libnetcdf: 4.7.4 xarray: 0.16.3.dev99+gc19467fb pandas: 1.3.1 numpy: 1.21.5 scipy: 1.7.1 netCDF4: 1.5.6 pydap: installed h5netcdf: 0.11.0 h5py: 2.10.0 Nio: None zarr: 2.8.3 cftime: 1.5.0 nc_time_axis: 1.3.1 PseudoNetCDF: installed rasterio: 1.2.6 cfgrib: None iris: 3.0.4 bottleneck: 1.3.2 dask: 2021.10.0 distributed: 2021.10.0 matplotlib: 3.4.3 cartopy: 0.19.0.post1 seaborn: 0.11.1 numbagg: 0.2.1 fsspec: 2021.11.1 cupy: None pint: 0.17 sparse: 0.12.0 setuptools: 49.6.0.post20210108 pip: 21.2.4 conda: None pytest: 6.2.4 IPython: 7.31.0 sphinx: 4.3.2
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/6384/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 957201551,MDU6SXNzdWU5NTcyMDE1NTE=,5655,Allow .attrs to use dict-likes,14371165,open,0,,,2,2021-07-31T08:31:55Z,2022-01-09T03:32:04Z,,MEMBER,,,," **Is your feature request related to a problem? Please describe.** Reading attributes from h5py-files is rather slow. So instead of retrieving it immediately I wanted to create a lazy dict-class that only retrieves the attribute values when necessary. But this is difficult to achieve since xarray keeps forcing the attrs to dicts in a lot of places. **Describe the solution you'd like** * Replace in https://github.com/pydata/xarray/blob/dddac11b01330791ffab4dfc72d226e71821973e/xarray/core/variable.py#L865 and https://github.com/pydata/xarray/blob/dddac11b01330791ffab4dfc72d226e71821973e/xarray/core/dataset.py#L798 with a `asdict(value)` function that checks if the input is a valid dict-like, if not convert to dict. Things that might be good to check: * `MutableMapping` * `hasattr(dict_like, ""copy"")` * `isinstance(dict_like, dict) == True` * Remove unneccessary conversions to dict. For example https://github.com/pydata/xarray/blob/dddac11b01330791ffab4dfc72d226e71821973e/xarray/core/merge.py#L523 should not be necessary as attrs from variables/dataarrays/datasets have already been forced to dicts when they were initialized. **Describe alternatives you've considered** * One could lazify with dicts as well, for example by replacing the value with a function. This however won't look good in reprs, that's why having a convienence class is nice. * `dict(LazyDict)` always forces to dict, it does not let it pass through unchanged even if `isinstance(LazyDict, dict) == True`. Interesting reading: https://stackoverflow.com/questions/16669367/setup-dictionary-lazily https://stackoverflow.com/questions/3387691/how-to-perfectly-override-a-dict ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5655/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue 931796211,MDU6SXNzdWU5MzE3OTYyMTE=,5546,Limit number of displayed dimensions in repr,14371165,closed,0,,,1,2021-06-28T17:25:18Z,2022-01-03T17:38:48Z,2022-01-03T17:38:48Z,MEMBER,,,," **What happened**: Dimension doesn't seem to be limited when there are too many of them. See example below. This slows down the repr significantly and is quite unreadable to me. **What you expected to happen**: To be limited so that it aligns with whatever the maximum line length is for variables. It's also fine if it continues on a couple of rows below in similar fashion to variables. **Minimal Complete Verifiable Example**: This is probably a bit of an edge case. My real datasets usually have around 12 ""dimensions"" and coords, +2000 variables, 50 attrs. ```python a = np.arange(0, 2000) data_vars = dict() for i in a: data_vars[f""long_variable_name_{i}""] = xr.DataArray( name=f""long_variable_name_{i}"", data=np.array([3, 4]), dims=[f""long_coord_name_{i}_x""], coords={f""long_coord_name_{i}_x"": np.array([0, 1])}, ) ds0 = xr.Dataset(data_vars) ds0.attrs = {f""attr_{k}"": 2 for k in a} ``` ```python Dimensions: (long_coord_name_0_x: 2, long_coord_name_1000_x: 2, long_coord_name_1001_x: 2, long_coord_name_1002_x: 2, long_coord_name_1003_x: 2, long_coord_name_1004_x: 2, long_coord_name_1005_x: 2, long_coord_name_1006_x: 2, long_coord_name_1007_x: 2, long_coord_name_1008_x: 2, long_coord_name_1009_x: 2, long_coord_name_100_x: 2, long_coord_name_1010_x: 2, long_coord_name_1011_x: 2, long_coord_name_1012_x: 2, long_coord_name_1013_x: 2, long_coord_name_1014_x: 2, long_coord_name_1015_x: 2, long_coord_name_1016_x: 2, long_coord_name_1017_x: 2, long_coord_name_1018_x: 2, long_coord_name_1019_x: 2, long_coord_name_101_x: 2, long_coord_name_1020_x: 2, long_coord_name_1021_x: 2, long_coord_name_1022_x: 2, long_coord_name_1023_x: 2, long_coord_name_1024_x: 2, long_coord_name_1025_x: 2, long_coord_name_1026_x: 2, long_coord_name_1027_x: 2, long_coord_name_1028_x: 2, long_coord_name_1029_x: 2, long_coord_name_102_x: 2, long_coord_name_1030_x: 2, long_coord_name_1031_x: 2, long_coord_name_1032_x: 2, long_coord_name_1033_x: 2, long_coord_name_1034_x: 2, long_coord_name_1035_x: 2, long_coord_name_1036_x: 2, long_coord_name_1037_x: 2, long_coord_name_1038_x: 2, long_coord_name_1039_x: 2, long_coord_name_103_x: 2, long_coord_name_1040_x: 2, long_coord_name_1041_x: 2, long_coord_name_1042_x: 2, long_coord_name_1043_x: 2, long_coord_name_1044_x: 2, long_coord_name_1045_x: 2, long_coord_name_1046_x: 2, long_coord_name_1047_x: 2, long_coord_name_1048_x: 2, long_coord_name_1049_x: 2, long_coord_name_104_x: 2, long_coord_name_1050_x: 2, long_coord_name_1051_x: 2, long_coord_name_1052_x: 2, long_coord_name_1053_x: 2, long_coord_name_1054_x: 2, long_coord_name_1055_x: 2, long_coord_name_1056_x: 2, long_coord_name_1057_x: 2, long_coord_name_1058_x: 2, long_coord_name_1059_x: 2, long_coord_name_105_x: 2, long_coord_name_1060_x: 2, long_coord_name_1061_x: 2, long_coord_name_1062_x: 2, long_coord_name_1063_x: 2, long_coord_name_1064_x: 2, long_coord_name_1065_x: 2, long_coord_name_1066_x: 2, long_coord_name_1067_x: 2, long_coord_name_1068_x: 2, long_coord_name_1069_x: 2, long_coord_name_106_x: 2, long_coord_name_1070_x: 2, long_coord_name_1071_x: 2, long_coord_name_1072_x: 2, long_coord_name_1073_x: 2, long_coord_name_1074_x: 2, long_coord_name_1075_x: 2, long_coord_name_1076_x: 2, long_coord_name_1077_x: 2, long_coord_name_1078_x: 2, long_coord_name_1079_x: 2, long_coord_name_107_x: 2, long_coord_name_1080_x: 2, long_coord_name_1081_x: 2, long_coord_name_1082_x: 2, long_coord_name_1083_x: 2, long_coord_name_1084_x: 2, long_coord_name_1085_x: 2, long_coord_name_1086_x: 2, long_coord_name_1087_x: 2, long_coord_name_1088_x: 2, long_coord_name_1089_x: 2, long_coord_name_108_x: 2, long_coord_name_1090_x: 2, long_coord_name_1091_x: 2, long_coord_name_1092_x: 2, long_coord_name_1093_x: 2, long_coord_name_1094_x: 2, long_coord_name_1095_x: 2, long_coord_name_1096_x: 2, long_coord_name_1097_x: 2, long_coord_name_1098_x: 2, long_coord_name_1099_x: 2, long_coord_name_109_x: 2, long_coord_name_10_x: 2, long_coord_name_1100_x: 2, long_coord_name_1101_x: 2, long_coord_name_1102_x: 2, long_coord_name_1103_x: 2, long_coord_name_1104_x: 2, long_coord_name_1105_x: 2, long_coord_name_1106_x: 2, long_coord_name_1107_x: 2, long_coord_name_1108_x: 2, long_coord_name_1109_x: 2, long_coord_name_110_x: 2, long_coord_name_1110_x: 2, long_coord_name_1111_x: 2, long_coord_name_1112_x: 2, long_coord_name_1113_x: 2, long_coord_name_1114_x: 2, long_coord_name_1115_x: 2, long_coord_name_1116_x: 2, long_coord_name_1117_x: 2, long_coord_name_1118_x: 2, long_coord_name_1119_x: 2, long_coord_name_111_x: 2, long_coord_name_1120_x: 2, long_coord_name_1121_x: 2, long_coord_name_1122_x: 2, long_coord_name_1123_x: 2, long_coord_name_1124_x: 2, long_coord_name_1125_x: 2, long_coord_name_1126_x: 2, long_coord_name_1127_x: 2, long_coord_name_1128_x: 2, long_coord_name_1129_x: 2, long_coord_name_112_x: 2, long_coord_name_1130_x: 2, long_coord_name_1131_x: 2, long_coord_name_1132_x: 2, long_coord_name_1133_x: 2, long_coord_name_1134_x: 2, long_coord_name_1135_x: 2, long_coord_name_1136_x: 2, long_coord_name_1137_x: 2, long_coord_name_1138_x: 2, long_coord_name_1139_x: 2, long_coord_name_113_x: 2, long_coord_name_1140_x: 2, long_coord_name_1141_x: 2, long_coord_name_1142_x: 2, long_coord_name_1143_x: 2, long_coord_name_1144_x: 2, long_coord_name_1145_x: 2, long_coord_name_1146_x: 2, long_coord_name_1147_x: 2, long_coord_name_1148_x: 2, long_coord_name_1149_x: 2, long_coord_name_114_x: 2, long_coord_name_1150_x: 2, long_coord_name_1151_x: 2, long_coord_name_1152_x: 2, long_coord_name_1153_x: 2, long_coord_name_1154_x: 2, long_coord_name_1155_x: 2, long_coord_name_1156_x: 2, long_coord_name_1157_x: 2, long_coord_name_1158_x: 2, long_coord_name_1159_x: 2, long_coord_name_115_x: 2, long_coord_name_1160_x: 2, long_coord_name_1161_x: 2, long_coord_name_1162_x: 2, long_coord_name_1163_x: 2, long_coord_name_1164_x: 2, long_coord_name_1165_x: 2, long_coord_name_1166_x: 2, long_coord_name_1167_x: 2, long_coord_name_1168_x: 2, long_coord_name_1169_x: 2, long_coord_name_116_x: 2, long_coord_name_1170_x: 2, long_coord_name_1171_x: 2, long_coord_name_1172_x: 2, long_coord_name_1173_x: 2, long_coord_name_1174_x: 2, long_coord_name_1175_x: 2, long_coord_name_1176_x: 2, long_coord_name_1177_x: 2, long_coord_name_1178_x: 2, long_coord_name_1179_x: 2, long_coord_name_117_x: 2, long_coord_name_1180_x: 2, long_coord_name_1181_x: 2, long_coord_name_1182_x: 2, long_coord_name_1183_x: 2, long_coord_name_1184_x: 2, long_coord_name_1185_x: 2, long_coord_name_1186_x: 2, long_coord_name_1187_x: 2, long_coord_name_1188_x: 2, long_coord_name_1189_x: 2, long_coord_name_118_x: 2, long_coord_name_1190_x: 2, long_coord_name_1191_x: 2, long_coord_name_1192_x: 2, long_coord_name_1193_x: 2, long_coord_name_1194_x: 2, long_coord_name_1195_x: 2, long_coord_name_1196_x: 2, long_coord_name_1197_x: 2, long_coord_name_1198_x: 2, long_coord_name_1199_x: 2, long_coord_name_119_x: 2, long_coord_name_11_x: 2, long_coord_name_1200_x: 2, long_coord_name_1201_x: 2, long_coord_name_1202_x: 2, long_coord_name_1203_x: 2, long_coord_name_1204_x: 2, long_coord_name_1205_x: 2, long_coord_name_1206_x: 2, long_coord_name_1207_x: 2, long_coord_name_1208_x: 2, long_coord_name_1209_x: 2, long_coord_name_120_x: 2, long_coord_name_1210_x: 2, long_coord_name_1211_x: 2, long_coord_name_1212_x: 2, long_coord_name_1213_x: 2, long_coord_name_1214_x: 2, long_coord_name_1215_x: 2, long_coord_name_1216_x: 2, long_coord_name_1217_x: 2, long_coord_name_1218_x: 2, long_coord_name_1219_x: 2, long_coord_name_121_x: 2, long_coord_name_1220_x: 2, long_coord_name_1221_x: 2, long_coord_name_1222_x: 2, long_coord_name_1223_x: 2, long_coord_name_1224_x: 2, long_coord_name_1225_x: 2, long_coord_name_1226_x: 2, long_coord_name_1227_x: 2, long_coord_name_1228_x: 2, long_coord_name_1229_x: 2, long_coord_name_122_x: 2, long_coord_name_1230_x: 2, long_coord_name_1231_x: 2, long_coord_name_1232_x: 2, long_coord_name_1233_x: 2, long_coord_name_1234_x: 2, long_coord_name_1235_x: 2, long_coord_name_1236_x: 2, long_coord_name_1237_x: 2, long_coord_name_1238_x: 2, long_coord_name_1239_x: 2, long_coord_name_123_x: 2, long_coord_name_1240_x: 2, long_coord_name_1241_x: 2, long_coord_name_1242_x: 2, long_coord_name_1243_x: 2, long_coord_name_1244_x: 2, long_coord_name_1245_x: 2, long_coord_name_1246_x: 2, long_coord_name_1247_x: 2, long_coord_name_1248_x: 2, long_coord_name_1249_x: 2, long_coord_name_124_x: 2, long_coord_name_1250_x: 2, 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long_coord_name_834_x: 2, long_coord_name_835_x: 2, long_coord_name_836_x: 2, long_coord_name_837_x: 2, long_coord_name_838_x: 2, long_coord_name_839_x: 2, long_coord_name_83_x: 2, long_coord_name_840_x: 2, long_coord_name_841_x: 2, long_coord_name_842_x: 2, long_coord_name_843_x: 2, long_coord_name_844_x: 2, long_coord_name_845_x: 2, long_coord_name_846_x: 2, long_coord_name_847_x: 2, long_coord_name_848_x: 2, long_coord_name_849_x: 2, long_coord_name_84_x: 2, long_coord_name_850_x: 2, long_coord_name_851_x: 2, long_coord_name_852_x: 2, long_coord_name_853_x: 2, long_coord_name_854_x: 2, long_coord_name_855_x: 2, long_coord_name_856_x: 2, long_coord_name_857_x: 2, long_coord_name_858_x: 2, long_coord_name_859_x: 2, long_coord_name_85_x: 2, long_coord_name_860_x: 2, long_coord_name_861_x: 2, long_coord_name_862_x: 2, long_coord_name_863_x: 2, long_coord_name_864_x: 2, long_coord_name_865_x: 2, long_coord_name_866_x: 2, long_coord_name_867_x: 2, long_coord_name_868_x: 2, long_coord_name_869_x: 2, long_coord_name_86_x: 2, long_coord_name_870_x: 2, long_coord_name_871_x: 2, long_coord_name_872_x: 2, long_coord_name_873_x: 2, long_coord_name_874_x: 2, long_coord_name_875_x: 2, long_coord_name_876_x: 2, long_coord_name_877_x: 2, long_coord_name_878_x: 2, long_coord_name_879_x: 2, long_coord_name_87_x: 2, long_coord_name_880_x: 2, long_coord_name_881_x: 2, long_coord_name_882_x: 2, long_coord_name_883_x: 2, long_coord_name_884_x: 2, long_coord_name_885_x: 2, long_coord_name_886_x: 2, long_coord_name_887_x: 2, long_coord_name_888_x: 2, long_coord_name_889_x: 2, long_coord_name_88_x: 2, long_coord_name_890_x: 2, long_coord_name_891_x: 2, long_coord_name_892_x: 2, long_coord_name_893_x: 2, long_coord_name_894_x: 2, long_coord_name_895_x: 2, long_coord_name_896_x: 2, long_coord_name_897_x: 2, long_coord_name_898_x: 2, long_coord_name_899_x: 2, long_coord_name_89_x: 2, long_coord_name_8_x: 2, long_coord_name_900_x: 2, long_coord_name_901_x: 2, long_coord_name_902_x: 2, long_coord_name_903_x: 2, long_coord_name_904_x: 2, long_coord_name_905_x: 2, long_coord_name_906_x: 2, long_coord_name_907_x: 2, long_coord_name_908_x: 2, long_coord_name_909_x: 2, long_coord_name_90_x: 2, long_coord_name_910_x: 2, long_coord_name_911_x: 2, long_coord_name_912_x: 2, long_coord_name_913_x: 2, long_coord_name_914_x: 2, long_coord_name_915_x: 2, long_coord_name_916_x: 2, long_coord_name_917_x: 2, long_coord_name_918_x: 2, long_coord_name_919_x: 2, long_coord_name_91_x: 2, long_coord_name_920_x: 2, long_coord_name_921_x: 2, long_coord_name_922_x: 2, long_coord_name_923_x: 2, long_coord_name_924_x: 2, long_coord_name_925_x: 2, long_coord_name_926_x: 2, long_coord_name_927_x: 2, long_coord_name_928_x: 2, long_coord_name_929_x: 2, long_coord_name_92_x: 2, long_coord_name_930_x: 2, long_coord_name_931_x: 2, long_coord_name_932_x: 2, long_coord_name_933_x: 2, long_coord_name_934_x: 2, long_coord_name_935_x: 2, long_coord_name_936_x: 2, long_coord_name_937_x: 2, long_coord_name_938_x: 2, long_coord_name_939_x: 2, long_coord_name_93_x: 2, long_coord_name_940_x: 2, long_coord_name_941_x: 2, long_coord_name_942_x: 2, long_coord_name_943_x: 2, long_coord_name_944_x: 2, long_coord_name_945_x: 2, long_coord_name_946_x: 2, long_coord_name_947_x: 2, long_coord_name_948_x: 2, long_coord_name_949_x: 2, long_coord_name_94_x: 2, long_coord_name_950_x: 2, long_coord_name_951_x: 2, long_coord_name_952_x: 2, long_coord_name_953_x: 2, long_coord_name_954_x: 2, long_coord_name_955_x: 2, long_coord_name_956_x: 2, long_coord_name_957_x: 2, long_coord_name_958_x: 2, long_coord_name_959_x: 2, long_coord_name_95_x: 2, long_coord_name_960_x: 2, long_coord_name_961_x: 2, long_coord_name_962_x: 2, long_coord_name_963_x: 2, long_coord_name_964_x: 2, long_coord_name_965_x: 2, long_coord_name_966_x: 2, long_coord_name_967_x: 2, long_coord_name_968_x: 2, long_coord_name_969_x: 2, long_coord_name_96_x: 2, long_coord_name_970_x: 2, long_coord_name_971_x: 2, long_coord_name_972_x: 2, long_coord_name_973_x: 2, long_coord_name_974_x: 2, long_coord_name_975_x: 2, long_coord_name_976_x: 2, long_coord_name_977_x: 2, long_coord_name_978_x: 2, long_coord_name_979_x: 2, long_coord_name_97_x: 2, long_coord_name_980_x: 2, long_coord_name_981_x: 2, long_coord_name_982_x: 2, long_coord_name_983_x: 2, long_coord_name_984_x: 2, long_coord_name_985_x: 2, long_coord_name_986_x: 2, long_coord_name_987_x: 2, long_coord_name_988_x: 2, long_coord_name_989_x: 2, long_coord_name_98_x: 2, long_coord_name_990_x: 2, long_coord_name_991_x: 2, long_coord_name_992_x: 2, long_coord_name_993_x: 2, long_coord_name_994_x: 2, long_coord_name_995_x: 2, long_coord_name_996_x: 2, long_coord_name_997_x: 2, long_coord_name_998_x: 2, long_coord_name_999_x: 2, long_coord_name_99_x: 2, long_coord_name_9_x: 2) Coordinates: (12/2000) * long_coord_name_0_x (long_coord_name_0_x) int32 0 1 * long_coord_name_1_x (long_coord_name_1_x) int32 0 1 * long_coord_name_2_x (long_coord_name_2_x) int32 0 1 * long_coord_name_3_x (long_coord_name_3_x) int32 0 1 * long_coord_name_4_x (long_coord_name_4_x) int32 0 1 * long_coord_name_5_x (long_coord_name_5_x) int32 0 1 ... * long_coord_name_1994_x (long_coord_name_1994_x) int32 0 1 * long_coord_name_1995_x (long_coord_name_1995_x) int32 0 1 * long_coord_name_1996_x (long_coord_name_1996_x) int32 0 1 * long_coord_name_1997_x (long_coord_name_1997_x) int32 0 1 * long_coord_name_1998_x (long_coord_name_1998_x) int32 0 1 * long_coord_name_1999_x (long_coord_name_1999_x) int32 0 1 Data variables: (12/2000) long_variable_name_0 (long_coord_name_0_x) int32 3 4 long_variable_name_1 (long_coord_name_1_x) int32 3 4 long_variable_name_2 (long_coord_name_2_x) int32 3 4 long_variable_name_3 (long_coord_name_3_x) int32 3 4 long_variable_name_4 (long_coord_name_4_x) int32 3 4 long_variable_name_5 (long_coord_name_5_x) int32 3 4 ... long_variable_name_1994 (long_coord_name_1994_x) int32 3 4 long_variable_name_1995 (long_coord_name_1995_x) int32 3 4 long_variable_name_1996 (long_coord_name_1996_x) int32 3 4 long_variable_name_1997 (long_coord_name_1997_x) int32 3 4 long_variable_name_1998 (long_coord_name_1998_x) int32 3 4 long_variable_name_1999 (long_coord_name_1999_x) int32 3 4 Attributes: (12/2000) attr_0: 2 attr_1: 2 attr_2: 2 attr_3: 2 attr_4: 2 attr_5: 2 ... attr_1994: 2 attr_1995: 2 attr_1996: 2 attr_1997: 2 attr_1998: 2 attr_1999: 2 ``` **Anything else we need to know?**: **Environment**:
Output of xr.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 3.8.8 | packaged by conda-forge | (default, Feb 20 2021, 15:50:08) [MSC v.1916 64 bit (AMD64)] python-bits: 64 OS: Windows OS-release: 10 byteorder: little LC_ALL: None LANG: en libhdf5: 1.10.6 libnetcdf: None xarray: 0.18.2 pandas: 1.2.4 numpy: 1.20.3 scipy: 1.6.3 netCDF4: None pydap: None h5netcdf: None h5py: 3.2.1 Nio: None zarr: None cftime: None nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: 1.3.2 dask: 2021.05.0 distributed: 2021.05.0 matplotlib: 3.4.2 cartopy: None seaborn: 0.11.1 numbagg: None pint: None setuptools: 49.6.0.post20210108 pip: 21.1.2 conda: 4.10.1 pytest: 6.2.4 IPython: 7.24.1 sphinx: 4.0.2
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5546/reactions"", ""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 1042698589,I_kwDOAMm_X84-JlFd,5928,Relax GitHub Actions first time contributor approval?,14371165,closed,0,,,2,2021-11-02T18:45:16Z,2021-11-02T21:44:54Z,2021-11-02T21:44:54Z,MEMBER,,,,"A while back GitHub made it so that new contributors cannot trigger GitHub Actions workflows and a maintainer has to hit ""Approve and Run"" every time they push a commit to their PR. This is rather annoying for both the contributor and the maintainer as the back and forth takes time. It however seems possible to relax this constraint: https://twitter.com/metcalfc/status/1448414192285806592?t=maeChQZTSUh2Ph0YFk-hGA&s=19 Shall we relax this constraint? ref: https://github.com/dask/community/issues/191","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/5928/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 778083748,MDU6SXNzdWU3NzgwODM3NDg=,4761,Dataset.interp drops boolean variables,14371165,closed,0,,,0,2021-01-04T13:09:56Z,2021-05-13T15:28:15Z,2021-05-13T15:28:15Z,MEMBER,,,," **What happened**: `Dataset.interp` silently drops boolean variables. **What you expected to happen**: If I'm interpolating a group of variables I expect to get all of them back in the correct shape with relevant values in them. If the variables are boolean or object arrays I don't expect it to do linear interpolation because it doesn't make sense but stepwise interpolation like nearest or zero order interpolation should be fine to expect. **Minimal Complete Verifiable Example**: ```python import numpy as np a = np.arange(0, 5) b = np.core.defchararray.add(""long_variable_name"", a.astype(str)) coords = dict(time=da.array([0, 1])) data_vars = dict() for v in b: data_vars[v] = xr.DataArray( name=v, data=np.array([0, 1]).astype(bool), dims=[""time""], coords=coords, ) ds1 = xr.Dataset(data_vars) # Print raw data: print(ds1) Out[3]: Dimensions: (time: 2) Coordinates: * time (time) int32 0 1 Data variables: long_variable_name0 (time) bool False True long_variable_name1 (time) bool False True long_variable_name2 (time) bool False True long_variable_name3 (time) bool False True long_variable_name4 (time) bool False True # Interpolate: ds1 = ds1.interp( time=da.array([0, 0.5, 1, 2]), assume_sorted=True, method=""nearest"", kwargs=dict(fill_value=""extrapolate""), ) # Print interpolated data: Dimensions: (time: 4) Coordinates: * time (time) float64 0.0 0.5 1.0 2.0 Data variables: *empty* ``` **Anything else we need to know?**: `ds.interp_like `use `ds.reindex` in these cases which seems like a good choice in `ds.interp` as well. But I think that both `ds.interp` and `ds.interp_like` should fill by default with nearest value instead of np.nan because we're still requesting interpolation. **Environment**:
Output of xr.show_versions() xr.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 3.8.5 (default, Sep 3 2020, 21:29:08) [MSC v.1916 64 bit (AMD64)] python-bits: 64 OS: Windows libhdf5: 1.10.4 libnetcdf: None xarray: 0.16.2 pandas: 1.1.5 numpy: 1.17.5 scipy: 1.4.1 netCDF4: None pydap: None h5netcdf: None h5py: 2.10.0 Nio: None zarr: None cftime: None nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: 1.3.2 dask: 2020.12.0 distributed: 2020.12.0 matplotlib: 3.3.2 cartopy: None seaborn: 0.11.1 numbagg: None pint: None setuptools: 51.0.0.post20201207 pip: 20.3.3 conda: 4.9.2 pytest: 6.2.1 IPython: 7.19.0 sphinx: 3.4.0
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4761/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 775875024,MDU6SXNzdWU3NzU4NzUwMjQ=,4739,Slow initilization of dataset.interp,14371165,closed,0,,,2,2020-12-29T12:46:05Z,2021-05-05T12:26:01Z,2021-05-05T12:26:01Z,MEMBER,,,," **What happened**: When interpolating a dataset with >2000 dask variables a lot of time is spent in `da.unifying_chunks` because `da.unifying_chunks` forces all variables and **coordinates** to a dask array. xarray on the other hand forces coordinates to pd.Index even if the coordinates was dask.array when the dataset was first created. **What you expected to happen**: If the coords of the dataset was initialized as dask arrays they should stay lazy. **Minimal Complete Verifiable Example**: ```python import xarray as xr import numpy as np import dask.array as da a = np.arange(0, 2000) b = np.core.defchararray.add(""long_variable_name"", a.astype(str)) coords = dict(time=da.array([0, 1])) data_vars = dict() for v in b: data_vars[v] = xr.DataArray( name=v, data=da.array([3, 4]), dims=[""time""], coords=coords ) ds0 = xr.Dataset(data_vars) ds0 = ds0.interp( time=da.array([0, 0.5, 1]), assume_sorted=True, kwargs=dict(fill_value=None), ) ``` **Anything else we need to know?**: Some thoughts: * Why can't coordinates be lazy? * Can we use dask.dataframe.Index instead of pd.Index when creating IndexVariables? * There's no time saved converting to dask arrays in `missing.interp_func`. But some time could be saved if we could convert them to dask arrays in `xr.Dataset.interp` before the variable loop starts. * Can we still store the dask array in IndexVariable and use a to_dask_array()-method to quickly get it? * Initializing the dataarrays will still be slow though since it still has to force the dask array to pd.Index. **Environment**:
Output of xr.show_versions() xr.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 3.8.5 (default, Sep 3 2020, 21:29:08) [MSC v.1916 64 bit (AMD64)] python-bits: 64 OS: Windows OS-release: 10 libhdf5: 1.10.4 libnetcdf: None xarray: 0.16.2 pandas: 1.1.5 numpy: 1.17.5 scipy: 1.4.1 netCDF4: None pydap: None h5netcdf: None h5py: 2.10.0 Nio: None zarr: None cftime: None nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: 1.3.2 dask: 2020.12.0 distributed: 2020.12.0 matplotlib: 3.3.2 cartopy: None seaborn: 0.11.1 numbagg: None pint: None setuptools: 51.0.0.post20201207 pip: 20.3.3 conda: 4.9.2 pytest: 6.2.1 IPython: 7.19.0 sphinx: 3.4.0
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4739/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 792639470,MDU6SXNzdWU3OTI2Mzk0NzA=,4839,Coordinate attributes are dropped when interpolating datasets,14371165,closed,0,,,3,2021-01-23T20:05:33Z,2021-04-27T07:00:08Z,2021-04-27T07:00:08Z,MEMBER,,,," **What happened**: When I was interpolating datasets I noticed that the coordinate variables disappeared. **What you expected to happen**: Coordinate attributes should be retained just like variables are. **Minimal Complete Verifiable Example**: ```python import numpy as np import xarray as xr names = np.core.defchararray.add(""long_variable_name"", np.arange(0, 2).astype(str)) coords = dict(time=np.array([0, 1])) data_vars = dict() for v in names: data_vars[v] = xr.Variable( ""time"", np.array([0, 1], dtype=int), attrs=dict(unit=""kg"") ) ds1 = xr.Dataset(data_vars=data_vars, coords=coords) ds1.attrs = { k: 2 for k in np.core.defchararray.add(""attr_"", np.arange(0, 3).astype(str)) } ds1.time.attrs.update(unit=""s"") # Print time: ds1.time Out[115]: array([0, 1]) Coordinates: * time (time) int32 0 1 Attributes: unit: s # Interpolate: ds1 = ds1.interp( time=np.array([0, 0.5, 1, 2]), assume_sorted=True, method=""linear"", kwargs=dict(fill_value=""extrapolate""), ) # Print interpolated time, units are lost: ds1.time Out[117]: array([0. , 0.5, 1. , 2. ]) Coordinates: * time (time) float64 0.0 0.5 1.0 2. ``` **Anything else we need to know?**: **Environment**:
Output of xr.show_versions() xr.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 3.8.5 (default, Sep 3 2020, 21:29:08) [MSC v.1916 64 bit (AMD64)] python-bits: 64 OS: Windows OS-release: 10 machine: AMD64 processor: Intel64 Family 6 Model 94 Stepping 3, GenuineIntel byteorder: little libhdf5: 1.10.4 libnetcdf: None xarray: 0.16.2 pandas: 1.2.0 numpy: 1.17.5 scipy: 1.4.1 netCDF4: None pydap: None h5netcdf: None h5py: 2.10.0 Nio: None zarr: None cftime: None nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: 1.3.2 dask: 2020.12.0 distributed: 2020.12.0 matplotlib: 3.3.2 cartopy: None seaborn: 0.11.1 numbagg: None pint: None setuptools: 51.1.2.post20210112 pip: 20.3.3 conda: 4.9.2 pytest: 6.2.1 IPython: 7.19.0 sphinx: 3.4.3
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4839/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 791725552,MDU6SXNzdWU3OTE3MjU1NTI=,4838,Simplify adding custom backends,14371165,closed,0,,,0,2021-01-22T06:02:53Z,2021-04-15T02:02:03Z,2021-04-15T02:02:03Z,MEMBER,,,," **Is your feature request related to a problem? Please describe.** I've been working on opening custom hdf formats in xarray, reading up on the apiv2 it is currently only possible to declare a new external plugin in setup.py but that doesn't seem easy or intuitive to me. **Describe the solution you'd like** Why can't we simply be allowed to add functions to the engine parameter? Example: ```python from custom_backend import engine ds = xr.load_dataset(filename, engine=engine) ``` This seems like a small function change to me from my initial _quick_ look because there's mainly a bunch of string checks in the normal case until we get to the registered backend functions, if we send in a function instead in the engine-parameter we can just bypass those checks. ","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4838/reactions"", ""total_count"": 2, ""+1"": 2, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 779938616,MDU6SXNzdWU3Nzk5Mzg2MTY=,4770,Interpolation always returns floats,14371165,open,0,,,1,2021-01-06T03:16:43Z,2021-01-12T16:30:54Z,,MEMBER,,,," **What happened**: When interpolating datasets integer arrays are forced to floats. **What you expected to happen**: To retain the same dtype after interpolation. **Minimal Complete Verifiable Example**: ```python import numpy as np import dask.array as da a = np.arange(0, 2) b = np.core.defchararray.add(""long_variable_name"", a.astype(str)) coords = dict(time=da.array([0, 1])) data_vars = dict() for v in b: data_vars[v] = xr.DataArray( name=v, data=da.array([0, 1], dtype=int), dims=[""time""], coords=coords, ) ds1 = xr.Dataset(data_vars) print(ds1) Out[35]: Dimensions: (time: 4) Coordinates: * time (time) float64 0.0 0.5 1.0 2.0 Data variables: long_variable_name0 (time) int32 dask.array long_variable_name1 (time) int32 dask.array # Interpolate: ds1 = ds1.interp( time=da.array([0, 0.5, 1, 2]), assume_sorted=True, method=""linear"", kwargs=dict(fill_value=""extrapolate""), ) # dask array thinks it's an integer array: print(ds1.long_variable_name0) Out[55]: dask.array Coordinates: * time (time) float64 0.0 0.5 1.0 2.0 # But once computed it turns out is a float: print(ds1.long_variable_name0.compute()) Out[38]: array([0. , 0.5, 1. , 2. ]) Coordinates: * time (time) float64 0.0 0.5 1.0 2.0 ``` **Anything else we need to know?**: An easy first step is to also force `np.float_` in `da.blockwise` in `missing.interp_func`. The more difficult way is to somehow be able to change back the dataarrays into the old dtype without affecting performance. I did a test simply adding `.astype() `to the returned value in `missing.interp` and it doubled the calculation time. I was thinking the conversion to floats in scipy could be avoided altogether by adding a (non-)public option to ignore any dtype checks and just let the user handle the ""unsafe"" interpolations. Related: https://github.com/scipy/scipy/issues/11093 **Environment**:
Output of xr.show_versions() xr.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 3.8.5 (default, Sep 3 2020, 21:29:08) [MSC v.1916 64 bit (AMD64)] python-bits: 64 OS: Windows libhdf5: 1.10.4 libnetcdf: None xarray: 0.16.2 pandas: 1.1.5 numpy: 1.17.5 scipy: 1.4.1 netCDF4: None pydap: None h5netcdf: None h5py: 2.10.0 Nio: None zarr: None cftime: None nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: 1.3.2 dask: 2020.12.0 distributed: 2020.12.0 matplotlib: 3.3.2 cartopy: None seaborn: 0.11.1 numbagg: None pint: None setuptools: 51.0.0.post20201207 pip: 20.3.3 conda: 4.9.2 pytest: 6.2.1 IPython: 7.19.0 sphinx: 3.4.0
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4770/reactions"", ""total_count"": 1, ""+1"": 1, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,,13221727,issue 775322346,MDU6SXNzdWU3NzUzMjIzNDY=,4736,Limit number of data variables shown in repr,14371165,closed,0,,,2,2020-12-28T10:15:26Z,2021-01-04T02:13:52Z,2021-01-04T02:13:52Z,MEMBER,,,," **What happened**: xarray feels very unresponsive when using datasets with >2000 data variables because it has to print all the 2000 variables everytime you print something to console. **What you expected to happen**: xarray should limit the number of variables printed to console. Maximum maybe 25? Same idea probably apply to dimensions, coordinates and attributes as well, pandas only shows 2 for reference, the first and last variables. **Minimal Complete Verifiable Example**: ```python import numpy as np import xarray as xr a = np.arange(0, 2000) b = np.core.defchararray.add(""long_variable_name"", a.astype(str)) data_vars = dict() for v in b: data_vars[v] = xr.DataArray( name=v, data=[3, 4], dims=[""time""], coords=dict(time=[0, 1]) ) ds = xr.Dataset(data_vars) # Everything above feels fast. Printing to console however takes about 13 seconds for me: print(ds) ``` **Anything else we need to know?**: Out of scope brainstorming: Though printing 2000 variables is probably madness for most people it is kind of nice to show all variables because you sometimes want to know what happened to a few other variables as well. Is there already an easy and fast way to create subgroup of the dataset, so we don' have to rely on the dataset printing everything to the console everytime? **Environment**:
Output of xr.show_versions() xr.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 3.8.5 (default, Sep 3 2020, 21:29:08) [MSC v.1916 64 bit (AMD64)] python-bits: 64 OS: Windows OS-release: 10 libhdf5: 1.10.4 libnetcdf: None xarray: 0.16.2 pandas: 1.1.5 numpy: 1.17.5 scipy: 1.4.1 netCDF4: None pydap: None h5netcdf: None h5py: 2.10.0 Nio: None zarr: None cftime: None nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: 1.3.2 dask: 2020.12.0 distributed: 2020.12.0 matplotlib: 3.3.2 cartopy: None seaborn: 0.11.1 numbagg: None pint: None setuptools: 51.0.0.post20201207 pip: 20.3.3 conda: 4.9.2 pytest: 6.2.1 IPython: 7.19.0 sphinx: 3.4.0
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4736/reactions"", ""total_count"": 2, ""+1"": 2, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 683954433,MDU6SXNzdWU2ODM5NTQ0MzM=,4367,Should __repr__ and __str__ be PEP8 compliant?,14371165,closed,0,,,7,2020-08-22T08:10:15Z,2020-11-25T23:25:44Z,2020-11-25T23:25:44Z,MEMBER,,,," **Is your feature request related to a problem? Please describe.** When creating docs with examples it would be nice if you could simply use `print(ds)` without being concerned about line lengths. **Describe the solution you'd like** Limit line length so that a method docstring in a class can use `print(ds)` without breaking PEP8 conventions. So maximum line length for the `ds.__repr__`/`ds.__str__` would be 79 - 4 - 4 = 71. **Example code** Example where print(ds) creates lines longer than 79: ```python import numpy as np import pandas as pd import xarray as xr class foo(): """""" Test class. (...) """""" def bar(): """""" Return 1. Examples -------- Create data: >>> np.random.seed(0) >>> temperature = 15 + 8 * np.random.randn(2, 2, 3) >>> precipitation = 10 * np.random.rand(2, 2, 3) >>> lon = [[-99.83, -99.32], [-99.79, -99.23]] >>> lat = [[42.25, 42.21], [42.63, 42.59]] >>> time = pd.date_range(""2014-09-06"", periods=3) >>> reference_time = pd.Timestamp(""2014-09-05"") Initialize a dataset with multiple dimensions: >>> ds = xr.Dataset( ... { ... ""temperature"": ([""x"", ""y"", ""time""], temperature), ... ""precipitation"": ([""x"", ""y"", ""time""], precipitation), ... }, ... coords={ ... ""lon"": ([""x"", ""y""], lon), ... ""lat"": ([""x"", ""y""], lat), ... ""time"": time, ... ""reference_time"": reference_time, ... }, ... ) Print results: >>> print(ds.temperature.values[0]) [[29.11241877 18.20125767 22.82990387] [32.92714559 29.94046392 7.18177696]] >>> print(ds) Dimensions: (time: 3, x: 2, y: 2) Coordinates: lon (x, y) float64 -99.83 -99.32 -99.79 -99.23 lat (x, y) float64 42.25 42.21 42.63 42.59 * time (time) datetime64[ns] 2014-09-06 2014-09-07 2014-09-08 reference_time datetime64[ns] 2014-09-05 Dimensions without coordinates: x, y Data variables: temperature (x, y, time) float64 29.11 18.2 22.83 ... 18.28 16.15 26.63 precipitation (x, y, time) float64 5.68 9.256 0.7104 ... 7.992 4.615 7.805 >>> ds Dimensions: (time: 3, x: 2, y: 2) Coordinates: lon (x, y) float64 -99.83 -99.32 -99.79 -99.23 lat (x, y) float64 42.25 42.21 42.63 42.59 * time (time) datetime64[ns] 2014-09-06 2014-09-07 2014-09-08 reference_time datetime64[ns] 2014-09-05 Dimensions without coordinates: x, y Data variables: temperature (x, y, time) float64 29.11 18.2 22.83 ... 18.28 16.15 26.63 precipitation (x, y, time) float64 5.68 9.256 0.7104 ... 7.992 4.615 7.805 """""" return 1 if __name__ == '__main__': import doctest doctest.testmod() ```","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4367/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue 679575175,MDU6SXNzdWU2Nzk1NzUxNzU=,4345,Improve Dataset documentation,14371165,closed,0,,,9,2020-08-15T13:27:33Z,2020-10-27T19:47:51Z,2020-10-27T19:47:51Z,MEMBER,,,,"**Is your feature request related to a problem? Please describe.** As a new user I find it difficult to get a new dataset initialized because the necessary parameters are not shown in the docstring. I have to google ""xarray dataset"" to get to http://xarray.pydata.org/en/stable/generated/xarray.Dataset.html to figure it out. In the figure below xarray.Dataset does not show the necessary parameters in the help pane: ![image](https://user-images.githubusercontent.com/14371165/90313058-aab66000-df09-11ea-9f3a-d2d3f2698b66.png) Compare to pandas.DataFrame that includes it: ![image](https://user-images.githubusercontent.com/14371165/90313083-e94c1a80-df09-11ea-8013-a7ffe90ab4d8.png) **Describe the solution you'd like** Looking at https://github.com/pydata/xarray/blob/master/xarray/core/dataset.py#L428 the `xr.Dataset.__init__.__doc__` does contain the necessary parameters so the suggestion is to simply move or copy that information up one level to` xr.Dataset.__doc__` For reference pandas does not use a docstring for the __init__ method: https://github.com/pandas-dev/pandas/blob/v1.1.0/pandas/core/frame.py#L339-L9257 The pandas docs also includes a few simple copy/pasteable examples on how to initialize. So a xarray example would be: ```python >>> import numpy as np >>> import xarray as xr >>> x = np.arange(4) >>> y = 2*x >>> ds = xr.Dataset({'y': (['x'], y)}, ... coords={'x': x}) >>> print(ds) Dimensions: (x: 4) Coordinates: * x (x) int32 0 1 2 3 Data variables: y (x) int32 0 2 4 6 ``` Or take some examples from http://xarray.pydata.org/en/stable/quick-overview.html#datasets or http://xarray.pydata.org/en/stable/data-structures.html#dataset although I found those a little bit confusing as they were dependent on previous results or rather complex with many dimensions. **Environment**:
Output of xr.show_versions() INSTALLED VERSIONS ------------------ commit: None python: 3.7.7 (default, May 6 2020, 11:45:54) [MSC v.1916 64 bit (AMD64)] python-bits: 64 OS: Windows OS-release: 10 machine: AMD64 processor: Intel64 Family 6 Model 58 Stepping 9, GenuineIntel byteorder: little LC_ALL: None LANG: en LOCALE: None.None libhdf5: 1.10.4 libnetcdf: None xarray: 0.15.0 pandas: 1.0.3 numpy: 1.18.1 scipy: 1.4.1 netCDF4: None pydap: None h5netcdf: None h5py: 2.10.0 Nio: None zarr: None cftime: None nc_time_axis: None PseudoNetCDF: None rasterio: None cfgrib: None iris: None bottleneck: 1.3.2 dask: 2.14.0 distributed: 2.22.0 matplotlib: 3.1.3 cartopy: None seaborn: 0.10.0 numbagg: None setuptools: 49.2.1.post20200807 pip: 20.2.1 conda: 4.8.3 pytest: 6.0.1 IPython: 7.17.0 sphinx: 3.2.0 C:\ProgramData\Anaconda3\lib\site-packages\setuptools\distutils_patch.py:26: UserWarning: Distutils was imported before Setuptools. This usage is discouraged and may exhibit undesirable behaviors or errors. Please use Setuptools' objects directly or at least import Setuptools first. ""Distutils was imported before Setuptools. This usage is discouraged ""
","{""url"": ""https://api.github.com/repos/pydata/xarray/issues/4345/reactions"", ""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,completed,13221727,issue